code: def generate_model_output_test1() -> Dict[str, torch._tensor.Tensor]:
      return {
          "predictions": torch.tensor(
              [
                  [
                      1.0,
                      0.0,
                      0.51,
                      0.8,
                      1.0,
                      0.0,
                      0.51,
                      0.8,
                      1.0,
                      0.0,
                      0.51,
                      0.8,
                  ]
              ]
          ),
          "session": torch.tensor(
              [[1, 1, 1, 1, 1, 1, 1, -1, -1, -1, -1, -1]]
          ),
          "labels": torch.tensor(
              [
                  [
                      0.9,
                      0.1,
                      0.2,
                      0.3,
                      0.9,
                      0.9,
                      0.0,
                      0.9,
                      0.1,
                      0.4,
                      0.9,
                      0.1,
                  ]
              ]
          ),
          "weights": torch.tensor(
              [
                  [
                      1.0,
                      1.0,
                      1.0,
                      1.0,
                      1.0,
                      1.0,
                      1.0,
                      1.0,
                      1.0,
                      1.0,
                      1.0,
                      1.0,
                  ]
              ]
          ),
          "expected_recall": torch.tensor([0.5]),
      }
  
file_path: torchrec/torchrec/metrics/tests/test_recall_session.py
imports: import unittest
  from typing import Dict, Optional, Union
  import torch
  from torch import no_grad
  from torchrec.metrics.metrics_config import RecTaskInfo, SessionMetricDef
  from torchrec.metrics.rec_metric import RecMetricException
  from torchrec.metrics.recall_session import RecallSessionMetric
label_desc: Use image node_id 1 for calling a global function with example usage: generate_model_output_test1() and returns: dict